A Study on the Effectiveness of Different Independent Component Analysis Algorithms for Hyperspectral Image Classification

This paper presents a thorough study on the performances of different independent component analysis (ICA) algorithms for the extraction of class-discriminant information in remote sensing hyperspectral image classification. The study considers the three implementations of ICA that are most widely used in signal processing, namely Infomax, FastICA, and JADE. The analysis aims to address a number of important issues regarding the use of ICA in the RS domain. Three scenarios are considered and the performances of the ICA algorithms are evaluated and compared against each other, in order to reach the final goal of identifying the most suitable approach to the analysis of hyperspectral images in supervised classification. Different feature extraction and selection techniques are used for dimensionality reduction with ICA and are then compared to the commonly used strategy, which is based on preprocessing data with principal components analysis (PCA) prior to classification. Experimental results obtained on three real hyperspectral data sets from each of the considered algorithms are presented and analyzed in terms of both classification accuracies and computational time.